Homework 5
Ann Gould
1
library(readr)
library(tidyverse)## ── Attaching packages ──────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.2.1 ✓ dplyr 0.8.4
## ✓ tibble 2.1.3 ✓ stringr 1.4.0
## ✓ tidyr 1.0.2 ✓ forcats 0.5.0
## ✓ purrr 0.3.3## ── Conflicts ─────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(dplyr)
library(ggplot2)
library(stringr) ames_liquor = readr::read_csv("2019_Iowa_Liquor_Sales.csv")## Parsed with column specification:
## cols(
## .default = col_double(),
## `Invoice/Item Number` = col_character(),
## Date = col_character(),
## `Store Name` = col_character(),
## Address = col_character(),
## City = col_character(),
## `Store Location` = col_character(),
## County = col_character(),
## `Category Name` = col_character(),
## `Vendor Number` = col_character(),
## `Vendor Name` = col_character(),
## `Item Description` = col_character()
## )## See spec(...) for full column specifications.ames_liquor2
# a
foo = ames_liquor %>% separate("Store Location", sep= "([\\?\\ ])", c(NA, "lat","long"))
foo$lat <- stringr::str_replace(foo$lat, '\\(', '')
foo$long <- stringr::str_replace(foo$lat, '\\)', '')
sample_n(foo, 5)# b
foo$lat <- as.numeric(foo$lat)
foo$long <- as.numeric(foo$long)
sample_n(foo, 5)# c
foo <- foo %>% separate("Date", sep= "([\\/])", c("month", "day", "year"))
sample_n(foo, 5)# other cleaning:
foo$"Bottles Sold" <- as.integer(foo$"Bottles Sold")3
# a
# removes NA locations so we can plot
foo2 = na.omit(foo)
# plot(foo2$lat, foo2$long, main = "Locations of stores", xlab="Latitude", ylab="Longitude")
ggplot(foo2, aes(x=lat, y=long)) + geom_point() + coord_fixed()
# 3b visual breakdown?? by liquor category name
ggplot(foo, aes (x=`Category Name`, color=`Volume Sold (Liters)`)) + geom_bar()
4
foo %>% summarise("num_sales" = sum(`Bottles Sold`),"volume_liquor" = sum(`Volume Sold (Liters)`), "spent" = sum(`Sale (Dollars)`))5
# ames_liquor_2019 <- foo %>% filter(year == 2019)
ggplot(foo, aes(x=day, y="Volume Sold (Liters)")) + geom_point() + geom_smooth()+ facet_wrap(~month)## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
It looks like there is more of a trend to purchase alcohol over the weekends (dots grouped by 2’s on dates that are Saturday/Sunday)
6
Fall football dates:
8-31-2019
9-14-2019
9-21-2019
10-5-2019
10-26-2019 – Homecoming Week
11-16-2019
11-23-2019
It looks like people are more likely to purchase alcohol for home football games. It looks like there were also more alcohol purchases for Fall Homecoming.